Lead the design and implementation of the Risk AI Platform (Risk OS) by establishing a scalable data, semantic, and
integration architecture that connects multiple AI-driven business applications through a common data layer, governance framework, metadata strategy, and shared services model. The role will define the target-state architecture for AI applications, Snowflake-based data assets, APIs, and enterprise integrations while ensuring solutions are production-ready, audit-ready, compliant, and aligned to enterprise technology standards. The architect will serve as a hands-on technical leader, bridging business teams building AI applications with IT, infrastructure, security, and data teams to accelerate industrialization and deployment of AI solutions.
Key Responsibilities
Define and evolve the enterprise architecture for the Risk AI Platform, including data models, semantic models,
metadata standards, integration patterns, API strategy, and shared data services across multiple AI
applications.
Design and implement a unified data architecture leveraging Snowflake as the central data layer, enabling reuse of common datasets, APIs, business entities, risk opinions, assessments, and historical records across applications.
Establish metadata, governance, lineage, and semantic standards using enterprise data governance practices and tools such as Collibra to improve interoperability, discoverability, and consistency of data assets.
Partner with business users, AI application teams, infrastructure, and IT teams to productionize AI-generated applications, including architecture reviews, deployment standards, code reviews, GitHub integration, UAT
support, and operational readiness.
Define integration standards for Snowflake, SharePoint, Bloomberg APIs, Azure services, web applications, AI agents, and future enterprise platforms while promoting reusable services and common architectural patterns.
Provide technical leadership and architectural guidance for AI, GenAI, agent-based solutions, MC -enabled architectures, and enterprise AI governance, ensuring scalability, security, compliance, and audit requirements are embedded into all solutions
Mentor architects and delivery teams with strong technical leadership
Own outcomes from vision to implementation, balancing business, technology, and risk.
Required Qualifications
16+ years in enterprise technology consulting
Architecture & Data: Enterprise Architecture, Data Architecture, Information Architecture, Semantic Modeling, Metadata Management, Data Governance, Canonical Data Modeling, Snowflake Architecture, API Design,
Integration Architecture, and Enterprise Platform Design.
AI & Technology: Generative AI Architecture, Agentic AI, MCP Frameworks, AI Application Productionization, Azure Cloud Services, GitHub, DevOps Practices, SharePoint Integration, API Management, Knowledge Graphs, and Enterprise AI Governance.
Leadership & Consulting: Strategic thinking, stakeholder management, architecture governance, advisory consulting, cross-functional collaboration, problem-solving, decision-making, communication with business and IT leadership, and the ability to define target-state architectures and implementation roadmaps in greenfield environments.
AI Platform & Enterprise Architecture, Data Architecture & Snowflake, Metadata, Semantic & Governance Architecture, Gen AI, Vibe coding